In today’s data-driven world, companies across the globe are plunging into the humungous pool of online customer information that is made available through different modes of data collection. More than 2.5 quintillion bytes of data are created each day, and this number just keeps on growing. Moreover, it was found that 67% of customers were willing to give their data for some benefits in terms of discounts.
Online customer data like social media activities, search engine tracking, and purchase history are being generated on a regular basis. This data gives eCommerce companies a peek into customer demographics, preferences & interests thus creating a customer profile useful for targeting specific products based on customer needs, just at the right time. Hence, data collection should be a priority for these companies to create customized marketing strategies.
“Analytical insights from data are only as useful as the quality of data collected.”
This brings us to the simple truth that accurate data collection plays a pivotal role in creating prolific marketing strategies yielding results for eCommerce companies.
Here are four reasons why collecting accurate customer data is useful for eCommerce companies:
- Promote relevant products through multiple communication channels like email, SMS, push & pull notifications, etc.
- Better customer segmentation by accurately dividing customers based on their behavior, preferences, and interests.
- Developing customized marketing campaigns to give a personal touch using information such as birthday, locality, age group, etc.
- Increasing customer lifetime value by predicting customer requirements based on purchase history information.
To ensure these strategies fall through as desired, you need to follow the basic principles of data collection.
“The basic principles of data collection include keeping things as simple as possible; planning the entire process of data selection, collection, analysis, and use from the start; and ensuring that any data collected is valid, reliable, and credible. It is also important that ethical issues are considered.”
The process and principles of data collection have been around for a long time; remember creating questionnaires and asking customers to fill them? Be it offline or online data collection, principles applied to remain the same.
Let’s have a look at the four principles of data collection one should follow from an online data collection perspective.
Principle 1: Identify the data you need to collect (Get the right data)
This principle simply states that the data needed for analysis should be fixed from the beginning of the data collection process. Do not collect data without figuring out how it will be analyzed or be useful. In such scenarios, it is highly likely you will end up with loads of data that is not useful. For e.g. auto part dealers need information about customer’s car models whereas fashion store owners need information about the customer’s age.
How to go about it?
The customer data you need depends on the type of business you run and the products you are selling. Hence, it is critical to understand what sort of data you need. Businesses usually collect the following type of data based on their requirement:
- Behavioral data
This data tells you how customers behave when they interact with your company. This data is measurable, analyzing it helps you improve your customer’s interaction with your company & increase conversion rates
Data types: Transactional data, communication data, online activity, customer relations
- Identity data
This data usually consists of customer demographics thereby helping you create an ideal customer profile. Thus, playing a vital role in forming customized marketing strategies.
Data types: Name, email, phone number, DOB, gender, birth date, social media profile, company’s name, position
- Descriptive data
This data consists of personal information apart from identity data. This data helps you understand the customer at a personal level and create appropriate strategies aimed at offering additional products and services e.g. a person buying a new car and being interested in music can help you cross-sell car speakers.
Data types: marital status, number of kids, lifestyle, hobbies, pets
- Qualitative data
This type of data is usually acquired through surveys. They include customer preferences, desirability, and sentiments. This data provides insight into the customer’s perception of your company.
Data types: Any kind of customer survey.
Principle 2: Authenticate your source of data (Get data the right way)
Getting data the right way encompasses the source of data. If the source is flawed, the data is meant to give you insignificant or irrelevant analytical insights, ultimately leading to a flawed marketing strategy. Hence, you need to choose the source of the required data very wisely.
How to go about it?
Here are a few sources you may need:
- Website analytical tools
If you are looking to analyze the behavioral data of your customer, a web analytic tool is an answer. Analyzing parameters such as bounce rates, page views, etc. can give you better insights into your customer’s interests.
- Online surveys
The requirement of consent and approval of the participant makes it the most authentic source of data collection. It gives you an in-depth analysis of customers’ opinions and sentiments. Ideal source for qualitative and descriptive data
- Customer interviews & feedback
It can be done through post-purchase feedback forms, call or video call interviews, or focus group meetings. It gives critical insight into customer’s preferences and opinions. It is ideal for identity, descriptive and qualitative data.
- Social media engagements
Customers present their unbiased views and preferences on social media platforms. Social listening can help you identify and analyze consumer’s interests, opinions, and experiences. It is ideal for identity and qualitative data.
Based on the data that you need to gather, a mixed-use of all these sources can help you create a successful marketing strategy.
Principle 3: Validate the data you collect for errors and reduce them (Get the data right)
No matter how authentic the source, there is still a minor possibility of errors based on the type of tools used for data collection. As the success of your marketing efforts depends on accuracy, following a proper data validation process is essential. For e.g. a customer marks his preference for a red shoe over a blue shoe in an online survey but social media listening may suggest that their favorite color is blue when it comes to outfits.
How to go about it?
Especially, when it comes to identity data like name, email, phone number, etc. accuracy and completeness are highly critical, and hence, validation is non-negotiable. Data validation and enrichment call for the implementation of data cleansing techniques.
Principle 4: Get current and updated data (Get the data right away)
Data degrades over time and leads to inaccurate and incorrect insights. Data such as email, phone number, company, position, and addresses of customers change over time. Even preferences and interests are associated with factors such as age, marital status, etc. hence bound to change. At this juncture, it is essential to ensure that the data collected is current and updated.
How to go about it?
Select data collection techniques and sources that can provide you with the latest information on your consumers. For e.g. government records have a high probability of containing obsolete data whereas customer interviews provide you with the latest information. Avoiding data degradation can also be done with the help of thorough cleansing and efficient data management.
Conclusion
Today, the data-oriented approach has left us with a massive amount of information at our disposal. Hence, the need to select the right data to collect, from an authentic source, with high accuracy is indispensable. As a business person or marketer, it is your prime responsibility to facilitate high-quality data collection, gain critical insights, and create impeccable marketing strategies customized according to the needs and wants of the customer.
